AI · Media AnalysisFull-Stack Engineering Case Study

Faithlence — AI Media Analysis

Upload audio or video and get a transcript and faith-based analysis. FFmpeg extracts the audio, Gemini transcribes and analyses it, and results are saved to MongoDB.

Audio & Video Transcription and AnalysisCompleted System
private build● completed
Faithlence — AI Media Analysis live interface preview
Technical Case Study

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Faithlence — AI Media Analysis

ProjectsExecutive Summary
01

Executive Summary

The Business Challenge

Sermons and teaching sessions are recorded as long audio and video files that are hard to search, summarise or reuse.

The Architectural Solution

A Next.js app that accepts any common audio or video format, extracts a clean audio track with FFmpeg, sends it to Gemini for transcription and analysis, and stores the results. Large uploads fall back to Vercel Blob storage.

Core Capabilities & System Highlights

Upload01

Any Media Format

Drag-and-drop MP3, WAV, M4A, MP4, MOV and more; audio is extracted automatically.

Gemini02

AI Transcription & Analysis

Gemini's multimodal model produces the transcript and a structured analysis.

Resilient03

Large File Handling

Hybrid upload flow with a Vercel Blob fallback for files too big for a single request.

Storage04

Saved Results

Transcripts and analyses are stored in MongoDB for later review.

02

System Architecture & Specs

App
Next.jsReactTailwind CSS

Next.js App Router

Upload UI and API routes that orchestrate extraction, analysis and storage.

Processing
FFmpegfluent-ffmpeg

FFmpeg Audio Extraction

fluent-ffmpeg with static FFmpeg binaries produces a clean audio track from any input.

AI
Gemini

Google Gemini

Multimodal transcription and analysis of the extracted audio.

Data
MongoDBMongooseVercel Blob

MongoDB + Vercel Blob

Results in MongoDB via Mongoose; large uploads staged in Vercel Blob.

Key Engineering Trade-offs & Operational Notes

01. Audio first

Extracting audio before analysis keeps uploads to the model small and fast.

02. Graceful large files

A hybrid upload flow avoids request-size limits on serverless hosts.

03. Typed end to end

TypeScript types cover the pipeline from upload to stored result.

03

Tech Stack & Role Matrix

Every library and framework in Faithlence — AI Media Analysis was chosen with intentional architectural trade-offs to balance speed, type safety, security, and scalability.

TechnologyCategorySystem RolePerformance Rationale
Next.jsFrameworkUpload UI and processing APIOne app for the interface and the pipeline.
FFmpegMediaAudio extractionHandles virtually any audio or video container.
GeminiAITranscription and analysisNative audio understanding in one API call.
MongoDBDatabaseResult storageFlexible documents for variable-length analyses.
04

Live Preview & Showcase

github.com
Faithlence — AI Media Analysis live homepage
Automatically capturedFaithlence — AI Media Analysis homepage previewOpen live product ↗
05

Metrics & Reliability

6+

Input formats

MP3, WAV, M4A, MP4, MOV and more

4 steps

Pipeline

Upload, extract, analyse, store

Gemini

AI model

Multimodal transcription and analysis

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